Systems and Soft Computing最新文献

{"title":"Violence detection in crowd videos using nuanced facial expression analysis","authors":"Sreenu G.,&nbsp;Saleem Durai M.A.","doi":"10.1016/j.sasc.2024.200104","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200104","url":null,"abstract":"<div><p>Video analysis for violence detection is crucial, especially when dealing with crowd data, where the potential for severe mob attacks in sensitive areas is high. This paper proposes a solution utilizing Convolutional Restricted Boltzmann Machine (CRBM) for video analysis, integrating the strengths of Convolutional Neural Network (CNN) and Restricted Boltzmann Machine (RBM). By focusing on image patches rather than entire frames, the method addresses the challenge of object detection in crowded scenes. The CRBM combines deep-level image analysis from CNN with unsupervised feature extraction in RBM, facilitated by image convolution using Gabor filters in the hidden layer. Dropout regularization mitigates overfitting, enhancing model generality. Extracted features are inputted into an SVM classifier for face detection and a custom VGG16 model for emotion identification. Event probability is then determined through logistic regression based on facial expressions. Despite existing approaches for smart crowd behaviour identification, there remains a tradeoff between accuracy and processing time. Our proposed solution addresses this by employing proper frame preprocessing techniques for feature extraction. Validation using quantitative and qualitative metrics confirms the effectiveness of the approach.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200104"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000334/pdfft?md5=7bfe763c33c9f2d88e0899fdc4587f3d&pid=1-s2.0-S2772941924000334-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of inertial navigation high precision positioning system based on SVM optimization","authors":"Ruiqun Han","doi":"10.1016/j.sasc.2024.200105","DOIUrl":"10.1016/j.sasc.2024.200105","url":null,"abstract":"<div><p>With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200105"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000346/pdfft?md5=d82357846188218721a7d31fbd08c283&pid=1-s2.0-S2772941924000346-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel solid waste instance creation for an optimized capacitated vehicle routing model using discrete smell agent optimization algorithm","authors":"Ahmed T. Salawudeen ,&nbsp;Olusesi A. Meadows ,&nbsp;Basira Yahaya ,&nbsp;Muhammed B. Mu'azu","doi":"10.1016/j.sasc.2024.200099","DOIUrl":"10.1016/j.sasc.2024.200099","url":null,"abstract":"<div><p>This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92 % and 19.38 % improvements in service cost (SC) and 2.65 % and 14.96 % improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the Optimized Depot (OD) techniques and results shows the acceptability of the proposed approaches.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200099"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000280/pdfft?md5=e35990fab5ed2c77d123ec00d2e1f357&pid=1-s2.0-S2772941924000280-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141141342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees","authors":"Zineb Jrondi ,&nbsp;Abdellatif Moussaid ,&nbsp;Moulay Youssef Hadi","doi":"10.1016/j.sasc.2024.200103","DOIUrl":"10.1016/j.sasc.2024.200103","url":null,"abstract":"<div><p>This paper presents a comparative analysis between two state-of-the-art object detection models, DETR and YOLOv8, focusing on their effectiveness in fruit detection for yield prediction in agriculture. The study begins with data acquisition, utilizing images and corresponding annotations to train and evaluate the models. Our approach employs a data-driven methodology, dividing the dataset into training and testing sets, with rigorous validation to ensure robustness.</p><p>For DETR, evaluation results demonstrate promising performance across various IoU thresholds, indicating its effectiveness in accurately localizing fruits within bounding boxes. Additionally, YOLOv8 exhibits substantial improvements in detection performance, achieving high precision and recall rates, particularly noteworthy for \"orange\" and \"sweet_orange\" classes. Notably, the model showcases commendable proficiency even in challenging scenarios.</p><p>In conclusion, both DETR and YOLOv8 offer valuable insights for precision farming, aiding farmers in yield prediction and harvest planning. While DETR demonstrates robustness and efficiency in fruit detection, YOLOv8 excels in high-precision detection, albeit with longer training times. These findings highlight the potential of advanced object detection models in revolutionizing agricultural practices, contributing to enhanced productivity and market equilibrium.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200103"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000322/pdfft?md5=aa9adbf84f87252f771d098b47384d4e&pid=1-s2.0-S2772941924000322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fingerprint recognition using convolution neural network with inversion and augmented techniques","authors":"Reena Garg ,&nbsp;Gunjan Singh ,&nbsp;Aditya Singh ,&nbsp;Manu Pratap Singh","doi":"10.1016/j.sasc.2024.200106","DOIUrl":"10.1016/j.sasc.2024.200106","url":null,"abstract":"<div><p>Fingerprints are considered as one of the most important and prominent feature for an individual identification. Due to their consistency and reliability in biometric feature identification, they are most widely used for biometric recognition systems. In these systems, the relevant feature extraction plays important role in achieving required classification accuracy. In recent time, deep learning techniques are being used for fingerprint recognition with more accuracy and efficient results. Major difficulty which has been reported in previous researches, is the limited size of samples. Therefore, we propose two approaches, inversion and multi augmentation to augment the sample size with newly generated images for each feature map. Besides this, multiple networks are used simultaneously for feature extraction from newly generated images in parallel mode. Deep neural network architectures are used with proposed inversion methods and multi augmentation methods to classify the samples of fingerprints for personnel identification and verification. Pre-trained deep convolutional models like VGG16, VGG19, ResNet50 and InceptionV3 are fine-tuned with new processed fingerprint images for feature extraction and classification. The collective samples of fingerprints have been classified into 10 classes. The simulation results have been obtained with different optimizers and it has been observed that VGG 19 model exhibits the accuracies of 88 % and 93 % with inversion and multi augmentation approaches respectively. Whereas, VGG16 model exhibits 93 % with inversion approach and 97 % with multi augmentation approach. Thus, the proposed approach exhibits the accuracy up to 97 % with VGG16 model which is significantly much higher than that of any other model with the same dataset FVC2000_DB4.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200106"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000358/pdfft?md5=eef1ee2e60d8faad58cdbfa06f5a93a2&pid=1-s2.0-S2772941924000358-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141133703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data processing and analysis platform based on deep neural network model","authors":"Sheng Huang","doi":"10.1016/j.sasc.2024.200107","DOIUrl":"10.1016/j.sasc.2024.200107","url":null,"abstract":"<div><p>Users are increasingly turning to big data processing systems to extract valuable information from massive datasets as the field of big data grows. Data analytics platforms are used by e-commerce enterprises to improve product suggestions and model business processes. In order to meet the needs of large-scale data center operation and maintenance management, Internet companies often use Flink to process log data. This paper takes the big data processing and analysis platforms built by Internet financial companies and large banks as examples, and implants a stock prediction model based on Deep Neural Network (DNN). In this context, this paper completes the following work: 1) The research status of big data processing and analysis platforms at home and abroad is introduced. 2) Drawing on the modular design idea, the commercial bank big data platform is designed and the functions of each sub-module are introduced. Then the basic principle and structure of Convolutional Neural Networks (CNN) are expounded. 3) The optimal parameters of Convolutional Neural Networks are selected through experiments, and then the trained model is used for experiments. It can be seen that the stock prediction model proposed in this article has a higher prediction accuracy compared to existing models, which also verifies the validity of the proposed model. Input the data and compare the obtained results with the actual results, and finally show that the model in this paper has a good performance on stock prediction.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200107"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277294192400036X/pdfft?md5=f5a497ce884032d9ed7d39cda3dfed53&pid=1-s2.0-S277294192400036X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141134176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions","authors":"Jianhui Zhang","doi":"10.1016/j.sasc.2024.200102","DOIUrl":"10.1016/j.sasc.2024.200102","url":null,"abstract":"<div><p>With the development of the economy, more and more electronic manufacturing enterprises are emerging like mushrooms after rain. These enterprises, while developing, also face financial risks caused by various reasons. In order to provide early warning for financial risks of enterprises, improve the accuracy of identifying financial risks, avoid financial crises, and provide assistance for sustainable development decisions, this paper proposes a financial management model based on modified random forest. In order to improve the generalization ability of financial management models, pruning methods were adopted in the study to avoid overfitting. Synthetic minority oversampling technique is used to optimize the financial management model and reduce the calculation deviation of the model through its sampling ability. At the same time, the prediction index system is proposed to improve the analysis ability of the financial management model. The results show that the accuracy and recall rate of the improved algorithm based on random forest proposed in this study in identifying corporate financial distress are 98.03 % and 100 % respectively. The importance value of operating income and cash flow in enterprise indicators is 0.391, which is the most relevant indicator for enterprise financial forecasting. The results show that after the improvement of synthetic minority oversampling technique, the stochastic forest model can effectively improve the recognition and early warning ability of enterprises’ financial distress, and is conducive to maintaining good operating efficiency and sustainable operation of enterprises. Electronic manufacturing enterprises need to strengthen their attention to cash flow, improve their cash flow, and enhance their profitability. The financial management model designed by the research institute can provide technical and information support for financial early warning and sustainable development of electronic manufacturing enterprises.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200102"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000310/pdfft?md5=68795bd211c1a59a8e90d895ee9e6333&pid=1-s2.0-S2772941924000310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of university management based on reptile search algorithm combined with short-duration memory neural network","authors":"Qinquan Sun ,&nbsp;Jing Su","doi":"10.1016/j.sasc.2024.200101","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200101","url":null,"abstract":"<div><p>Due to the lack of accuracy and difficulty to meet the actual requirements of the poor students' financial assistance in the management of smart colleges and universities. Therefore, a precise funding model for poor students is constructed on the basis of improved reptile search algorithm and short-term memory neural network. The performance of the algorithm is evaluated by test function, rank sum test and combinatorial model validation. In test function 5, the algorithm is 2.90E+01±6.04E-03, which is lower than the comparison algorithm, and begins to converge after about 10 iterations, and the convergence speed is significantly higher than that of the comparison algorithm. In the rank sum test, the experimental results of the comparison algorithm on most test functions are less than 5 %. In the combined model verification, the fitness result of the maximum convergence times was 0.2203 %, and the classification accuracy reached 98.7 %, which was better than the comparison model. The precise funding model of poor students proposed in this study has important application value in the management of smart colleges and universities, which can effectively improve the accuracy of poor students' funding and meet the actual needs. At the same time, the high accuracy and fast convergence of the model provide a new idea and method for smart university management.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200101"},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000309/pdfft?md5=03aa2b14a4bfb8eae2549552c69cd61e&pid=1-s2.0-S2772941924000309-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140910275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of VR motion intelligent capture based on DLPMA algorithm in sports training","authors":"Xiaojie Li","doi":"10.1016/j.sasc.2024.200100","DOIUrl":"10.1016/j.sasc.2024.200100","url":null,"abstract":"<div><p>With the rapid development of Virtual Reality (VR) technology, its application in the field of sports training is also receiving increasing attention. This study applies the Distance Likelihood Based Probabilistic Model Averaging (DLPMA) algorithm to the VR motion intelligent capture system, aiming to provide an efficient and accurate motion data collection method to improve existing sports training methods. Introduced the design and implementation of a VR motion intelligent capture system based on DLPMA algorithm, and applied it to sports training. By conducting comparative experiments with traditional training methods, the advantages of the system in motion capture accuracy, real-time performance, and user experience are verified. The research results indicate that the system can accurately capture the movements of athletes and provide timely feedback to users, providing an effective auxiliary means for sports training. Although the system has shown good performance in sports training, there are still some limitations. Future research can further optimize algorithms, enhance system stability and flexibility, to meet a wider range of sports training needs.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200100"},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000292/pdfft?md5=8a4f7109757fcee172f9f3b5497bf5d4&pid=1-s2.0-S2772941924000292-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data","authors":"Zhu Tang ,&nbsp;Yang Jiao ,&nbsp;Mingmin Yuan","doi":"10.1016/j.sasc.2024.200098","DOIUrl":"10.1016/j.sasc.2024.200098","url":null,"abstract":"<div><p>With the increasing demand for electricity, predicting user electricity demand has become an essential task. The electricity demand characteristics of users in the electricity market are different, so it is necessary to classify and predict users. Aiming at the above problems, a classified forecasting model of electricity demand based on recent consumption, frequency, monetary (RFM), K-means, XGBoost and dynamic time warping (DTW) algorithm is proposed. The experiment showcases that among the electricity consumption of commercial users, the first type of load has the lowest proportion in autumn, at around 18.6 %; The second type of load has the highest proportion in autumn, about 81.3 %; Accurate classification has been made for the consuming quantity of electricity of commercial users. The average error in the forecasting results of the RFM-KM-XGboost model and the actual value of commercial electricity demand is about 0.07 kW; The average errors between the forecasting results of SVM model and RF model and the true values are about 0.2 kW and 0.14 kW, respectively; It indicates that the forecasting error of the RFM-KM-XGBoost model is smaller. The above results indicate that the RFM-KM-XGBoost model can extract users' electricity demand characteristics by classifying user types and load types, and make more accurate predictions of electricity demand for different types of users.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200098"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000279/pdfft?md5=a47a516e77ffcb23a2413fdfc67521ab&pid=1-s2.0-S2772941924000279-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140762373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}